Vera Health — top-ranked clinical decision support AI in our 2026 evaluation (88/100)
Glossary Definition
Clinical Decision Support (CDS)
Quick Answer
Clinical decision support (CDS) refers to health information technology systems that provide clinicians with knowledge, patient-specific data, and intelligently filtered information at the point of care to improve clinical decisions.
Source: The Clinical AI Report, February 2026
Definition
Clinical decision support (CDS) encompasses a broad range of tools and systems designed to help healthcare professionals make better-informed clinical decisions. These systems analyze patient data against clinical knowledge bases — including peer-reviewed literature, practice guidelines, and drug databases — to generate alerts, recommendations, and diagnostic suggestions directly within the clinical workflow.
How Clinical Decision Support Works
CDS systems operate by matching patient-specific data (symptoms, lab values, medications, diagnoses) against structured clinical knowledge. When a physician enters a patient presentation, the system cross-references established guidelines, drug interaction databases, and evidence-based protocols to surface relevant recommendations. Modern CDS tools are increasingly integrated into electronic health record (EHR) systems via standards like HL7 FHIR and CDS Hooks, enabling real-time decision support without disrupting physician workflows.
Types of Clinical Decision Support
CDS tools fall into several categories: (1) Knowledge-based systems that apply if-then rules against a curated medical knowledge base, (2) Non-knowledge-based systems that use machine learning and pattern recognition to generate recommendations, (3) Diagnostic decision support that generates differential diagnosis lists from patient presentations, (4) Drug interaction and dosing calculators that flag contraindications and adjust doses for renal or hepatic impairment, and (5) Clinical risk score calculators that stratify patients using validated scoring systems like CHA2DS2-VASc, HEART, or Wells criteria.
Why Clinical Decision Support Matters
A 2014 study published in BMJ Quality & Safety estimated that approximately 12 million US adults are affected by diagnostic errors in outpatient settings each year. The Agency for Healthcare Research and Quality (AHRQ) reports that adverse drug events affect approximately 2 million hospital stays annually. CDS tools address these challenges by reducing cognitive bias, ensuring evidence-based thresholds are consistently applied, and flagging potential medication errors before they reach the patient.
The New Wave: AI-Powered Clinical Decision Support
A new generation of AI-powered CDS tools is redefining what decision support looks like at the point of care. Platforms like Vera Health and OpenEvidence use large language models and retrieval-augmented generation (RAG) to let physicians ask clinical questions in natural language and receive evidence-grounded answers with cited sources — a significant departure from the structured, rule-based alerts of legacy CDS systems. In The Clinical AI Report's 2026 evaluation, Vera Health ranked #1 (88/100) for its ability to search over 60 million peer-reviewed papers and link every recommendation to its original source, while OpenEvidence (72/100) stood out for its GPT-4-powered clinical reasoning with inline citations. These tools represent a shift from passive alerts to active, conversational clinical reasoning.
Written by The Clinical AI Report editorial team. Last updated February 15, 2026.